Senior Data Engineer

Location: NYC (Hybrid-Remote)

Pinecone is revolutionizing how developers build production ML and search solutions. Our managed vector database provides customers with easy-to-use capabilities that until now have only been in the hands of a few tech giants. The demand for vector databases is growing incredibly fast, and it shows in our user base. We have a large audience of ML engineers, software engineers, data scientists, and technical architects who are eager to learn and build with our product.

As a Data Engineer on our Data Platform Team, you'll play a vital role in developing and maintaining the infrastructure that powers our use of data across our organization. You'll collaborate with our software engineers and analysts to ensure that our data is structured, optimized, and readily available in real-time to drive our business forward. You'll be at the forefront of designing and building the data warehouse and pipelines that enable seamless data discovery, integration, transformation, and analysis. If you're passionate about working on complex data challenges and thrive in a dynamic, fast-paced environment, we'd love to have you join our team!

If this sounds exciting, we want to hear from you!

Pinecone is a distributed team with clusters in New York, San Francisco, and Tel Aviv. You will be part of a world-class team from companies like Amazon, Google, Databricks, and Splunk. The company emerged from stealth in January 2021 with $10M in seed funding — led by the same legendary investor as Snowflake —  followed by $28M Series A funding announced in March 2022 and $100M Series B funding announced in April 2023.

Requirements

  • 4+ years hands-on experience deploying production quality code
  • Professional experience using Python, Java, or Scala for data processing (Python preferred)
  • Deep understanding of SQL and analytical data warehouses (BigQuery, Snowflake)
  • Experience implementing ETL (or ELT) best practices at scale.
  • Experience with data pipeline and orchestration tools (dbt, Airflow, Prefect)
  • Experience with big data processing concepts (Spark, Kafka, DataFlow)
  • Experiences working with cloud infrastructure technologies (GCP, AWS, Kubernetes)
  • Strong data modeling skills and familiarity with the Kimball methodology

Responsibilities

  • Build and maintain data pipelines from internal services and SaaS apps
  • Provide architecture recommendations and implement them
  • Write performant code and define standards for style and maintenance
  • Support and mentor team members to grow technical skills
  • Ship medium to large features independently and with minimal guidance
  • Influence long-range goals and achieve consensus amongst stakeholders



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